Abstract
Using a novel stochastic programming (SP) formulation, we develop an algorithm for inventory control in industrial-size assemble-to-order (ATO) systems that has unparalleled efficiency and scalability. Applying our algorithm to several numerical examples, we generate new insights regarding the control and optimization of these systems. We consider a continuous time model, seeking base-stock levels for components that minimize the sum of holding costs and product-specific backorder costs. Our initial focus is on first-come, first-served (FCFS) allocation of components to products; for this setting our algorithm quickly computes solutions for which the asymptotic optimality gap with the optimal FCFS base-stock policy is less than 1%. We then turn to two related questions: How do common heuristics used in practice compare to our performance, and how costly is the FCFS assumption For the first question, we investigate the effectiveness of ignoring simultaneous stock-outs (ISS), a heuristic that has been used by companies such as IBM and Dell. Our experiments indicate that ISS performance, when compared to the optimal FCFS base-stock policy, improves as the average newsvendor (NV) fractiles increase but suffers under lead-time demand correlations. For the second question, we develop an efficiently computable upper bound on the benefit of optimal allocation over FCFS. We find that for many large ATO systems, FCFS performs surprisingly well and that its performance improves with decreasing NV fractile asymmetry among products and, again, with increasing average NV fractiles. We also investigate simple no-holdback allocation policies and find that they tend to outperform the best FCFS policies.
| Original language | English |
|---|---|
| Pages (from-to) | 544-560 |
| Number of pages | 17 |
| Journal | INFORMS Journal on Computing |
| Volume | 27 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Jun 2015 |
| Externally published | Yes |
Fingerprint
Keywords
- Base-stock policies
- Continuous-time assemble-to-order systems
- First-come-first-served allocation
- Industrial-scale problems
- Optimal allocation
- Stochastic programming
Cite this
}
Optimization of industrial-scale assemble-to-order systems. / van Jaarsveld, W.; Scheller-Wolf, A.
In: INFORMS Journal on Computing, Vol. 27, No. 3, 01.06.2015, p. 544-560.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Optimization of industrial-scale assemble-to-order systems
AU - van Jaarsveld, W.
AU - Scheller-Wolf, A.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Using a novel stochastic programming (SP) formulation, we develop an algorithm for inventory control in industrial-size assemble-to-order (ATO) systems that has unparalleled efficiency and scalability. Applying our algorithm to several numerical examples, we generate new insights regarding the control and optimization of these systems. We consider a continuous time model, seeking base-stock levels for components that minimize the sum of holding costs and product-specific backorder costs. Our initial focus is on first-come, first-served (FCFS) allocation of components to products; for this setting our algorithm quickly computes solutions for which the asymptotic optimality gap with the optimal FCFS base-stock policy is less than 1%. We then turn to two related questions: How do common heuristics used in practice compare to our performance, and how costly is the FCFS assumption For the first question, we investigate the effectiveness of ignoring simultaneous stock-outs (ISS), a heuristic that has been used by companies such as IBM and Dell. Our experiments indicate that ISS performance, when compared to the optimal FCFS base-stock policy, improves as the average newsvendor (NV) fractiles increase but suffers under lead-time demand correlations. For the second question, we develop an efficiently computable upper bound on the benefit of optimal allocation over FCFS. We find that for many large ATO systems, FCFS performs surprisingly well and that its performance improves with decreasing NV fractile asymmetry among products and, again, with increasing average NV fractiles. We also investigate simple no-holdback allocation policies and find that they tend to outperform the best FCFS policies.
AB - Using a novel stochastic programming (SP) formulation, we develop an algorithm for inventory control in industrial-size assemble-to-order (ATO) systems that has unparalleled efficiency and scalability. Applying our algorithm to several numerical examples, we generate new insights regarding the control and optimization of these systems. We consider a continuous time model, seeking base-stock levels for components that minimize the sum of holding costs and product-specific backorder costs. Our initial focus is on first-come, first-served (FCFS) allocation of components to products; for this setting our algorithm quickly computes solutions for which the asymptotic optimality gap with the optimal FCFS base-stock policy is less than 1%. We then turn to two related questions: How do common heuristics used in practice compare to our performance, and how costly is the FCFS assumption For the first question, we investigate the effectiveness of ignoring simultaneous stock-outs (ISS), a heuristic that has been used by companies such as IBM and Dell. Our experiments indicate that ISS performance, when compared to the optimal FCFS base-stock policy, improves as the average newsvendor (NV) fractiles increase but suffers under lead-time demand correlations. For the second question, we develop an efficiently computable upper bound on the benefit of optimal allocation over FCFS. We find that for many large ATO systems, FCFS performs surprisingly well and that its performance improves with decreasing NV fractile asymmetry among products and, again, with increasing average NV fractiles. We also investigate simple no-holdback allocation policies and find that they tend to outperform the best FCFS policies.
KW - Base-stock policies
KW - Continuous-time assemble-to-order systems
KW - First-come-first-served allocation
KW - Industrial-scale problems
KW - Optimal allocation
KW - Stochastic programming
UR - http://www.scopus.com/inward/record.url?scp=84947264151&partnerID=8YFLogxK
U2 - 10.1287/ijoc.2015.0641
DO - 10.1287/ijoc.2015.0641
M3 - Article
AN - SCOPUS:84947264151
VL - 27
SP - 544
EP - 560
JO - INFORMS Journal on Computing
JF - INFORMS Journal on Computing
SN - 1091-9856
IS - 3
ER -